{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkzOKBirz271"
      },
      "source": [
        "# Anomaly detection with embeddings\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/anomaly_detection.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "025c6c81c576"
      },
      "source": [
        "<!-- Princing warning Badge -->\n",
        "<table>\n",
        "  <tr>\n",
        "    <!-- Emoji -->\n",
        "    <td bgcolor=\"#f5949e\">\n",
        "      <font size=30>⚠️</font>\n",
        "    </td>\n",
        "    <!-- Text Content Cell -->\n",
        "    <td bgcolor=\"#f5949e\">\n",
        "      <h3><font color=black>This notebook requires paid tier rate limits to run properly.<br>  \n",
        "(cf. <a href=\"https://ai.google.dev/pricing#veo2\"><font color='#217bfe'>pricing</font></a> for more details).</font></h3>\n",
        "    </td>\n",
        "  </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQPvHyHCz7mk"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to use the embeddings from the Gemini API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and detect outliers outside a particular radius of the central point of each categorical cluster.\n",
        "\n",
        "For more information on getting started with embeddings generated from the Gemini API, check out the [Get Started](../quickstarts/Get_started.ipynb).\n",
        "\n",
        "## Prerequisites\n",
        "\n",
        "You can run this quickstart in Google Colab.\n",
        "\n",
        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
        "\n",
        "-  Python 3.11+\n",
        "-  An installation of `jupyter` to run the notebook.\n",
        "\n",
        "## Setup\n",
        "\n",
        "First, download and install the Gemini API Python library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LyLLYVEhzud8"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q google-genai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z5GJi99k0Ctz"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from google import genai\n",
        "from google.genai import types\n",
        "\n",
        "# Used to securely store your API key\n",
        "from google.colab import userdata\n",
        "\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.manifold import TSNE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yi0kitgd5aLG"
      },
      "source": [
        "### Grab an API Key\n",
        "\n",
        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
        "\n",
        "<a class=\"button button-primary\" href=\"https://aistudio.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
        "\n",
        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `GEMINI_API_KEY`.\n",
        "\n",
        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
        "\n",
        "* Put the key in the `GEMINI_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
        "* Pass the key to `genai.Client(api_key=...)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OeEZ5Bj5Zr8"
      },
      "outputs": [],
      "source": [
        "# Or use `os.getenv('GEMINI_API_KEY')` to fetch an environment variable.\n",
        "GEMINI_API_KEY=userdata.get('GEMINI_API_KEY')\n",
        "\n",
        "client = genai.Client(api_key=GEMINI_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ce6MdcP170Uv"
      },
      "source": [
        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "h3mqsrUB7zsE"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "models/embedding-001\n",
            "models/text-embedding-004\n",
            "models/gemini-embedding-exp-03-07\n",
            "models/gemini-embedding-exp\n",
            "models/gemini-embedding-001\n"
          ]
        }
      ],
      "source": [
        "for m in client.models.list():\n",
        "  if 'embedContent' in m.supported_actions:\n",
        "    print(m.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6fa32bbaa20"
      },
      "source": [
        "### Select the model to be used"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "fc0698cc90e2"
      },
      "outputs": [],
      "source": [
        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhWtEhZ6BO58"
      },
      "source": [
        "## Prepare the dataset\n",
        "\n",
        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. This tutorial uses the training subset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "YtHABp9BBTIt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['alt.atheism',\n",
              " 'comp.graphics',\n",
              " 'comp.os.ms-windows.misc',\n",
              " 'comp.sys.ibm.pc.hardware',\n",
              " 'comp.sys.mac.hardware',\n",
              " 'comp.windows.x',\n",
              " 'misc.forsale',\n",
              " 'rec.autos',\n",
              " 'rec.motorcycles',\n",
              " 'rec.sport.baseball',\n",
              " 'rec.sport.hockey',\n",
              " 'sci.crypt',\n",
              " 'sci.electronics',\n",
              " 'sci.med',\n",
              " 'sci.space',\n",
              " 'soc.religion.christian',\n",
              " 'talk.politics.guns',\n",
              " 'talk.politics.mideast',\n",
              " 'talk.politics.misc',\n",
              " 'talk.religion.misc']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
        "\n",
        "# View list of class names for dataset\n",
        "newsgroups_train.target_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPKgmQDQC3zd"
      },
      "source": [
        "Here is the first example in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "CSXYP0JwBXHh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lines: 15\n",
            "\n",
            " I was wondering if anyone out there could enlighten me on this car I saw\n",
            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
            "the front bumper was separate from the rest of the body. This is \n",
            "all I know. If anyone can tellme a model name, engine specs, years\n",
            "of production, where this car is made, history, or whatever info you\n",
            "have on this funky looking car, please e-mail.\n",
            "\n",
            "Thanks,\n",
            "- IL\n",
            "   ---- brought to you by your neighborhood Lerxst ----\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "idx = newsgroups_train.data[0].index('Lines')\n",
        "print(newsgroups_train.data[0][idx:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Raafa2naC6Ec"
      },
      "outputs": [],
      "source": [
        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \"\n",
        "\n",
        "# Cut off each text entry after 5,000 characters\n",
        "newsgroups_train.data = [d[0:5000] if len(d) > 5000 else d for d in newsgroups_train.data]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "ZjE_Lsr6IhEd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Label</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
              "      <td>7</td>\n",
              "      <td>rec.autos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SI Clock Poll - Final Call\\nSummary: Final ca...</td>\n",
              "      <td>4</td>\n",
              "      <td>comp.sys.mac.hardware</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PB questions...\\nOrganization: Purdue Univers...</td>\n",
              "      <td>4</td>\n",
              "      <td>comp.sys.mac.hardware</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Re: Weitek P9000 ?\\nOrganization: Harris Comp...</td>\n",
              "      <td>1</td>\n",
              "      <td>comp.graphics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Re: Shuttle Launch Question\\nOrganization: Sm...</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11309</th>\n",
              "      <td>Re: Migraines and scans\\nDistribution: world...</td>\n",
              "      <td>13</td>\n",
              "      <td>sci.med</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11310</th>\n",
              "      <td>Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...</td>\n",
              "      <td>4</td>\n",
              "      <td>comp.sys.mac.hardware</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11311</th>\n",
              "      <td>Mounting CPU Cooler in vertical case\\nOrganiz...</td>\n",
              "      <td>3</td>\n",
              "      <td>comp.sys.ibm.pc.hardware</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11312</th>\n",
              "      <td>Re: Sphere from 4 points?\\nOrganization: Cent...</td>\n",
              "      <td>1</td>\n",
              "      <td>comp.graphics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11313</th>\n",
              "      <td>stolen CBR900RR\\nOrganization: California Ins...</td>\n",
              "      <td>8</td>\n",
              "      <td>rec.motorcycles</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11314 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    Text  Label  \\\n",
              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
              "...                                                  ...    ...   \n",
              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
              "\n",
              "                     Class Name  \n",
              "0                     rec.autos  \n",
              "1         comp.sys.mac.hardware  \n",
              "2         comp.sys.mac.hardware  \n",
              "3                 comp.graphics  \n",
              "4                     sci.space  \n",
              "...                         ...  \n",
              "11309                   sci.med  \n",
              "11310     comp.sys.mac.hardware  \n",
              "11311  comp.sys.ibm.pc.hardware  \n",
              "11312             comp.graphics  \n",
              "11313           rec.motorcycles  \n",
              "\n",
              "[11314 rows x 3 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Put training points into a dataframe\n",
        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
        "df_train['Label'] = newsgroups_train.target\n",
        "# Match label to target name index\n",
        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
        "\n",
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7OHvTBaImpB"
      },
      "source": [
        "Next, sample some of the data by taking 150 data points in the training dataset and choosing a few categories. This tutorial uses the science categories."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "yPxwl05BIjWX"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_100019/406673449.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  .apply(lambda x: x.sample(SAMPLE_SIZE))\n"
          ]
        },
        {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1650</td>\n",
              "      <td>Privacy &amp; Anonymity on the Internet FAQ \\nSup...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1651</td>\n",
              "      <td>The source of that announcement\\nOrganization...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1652</td>\n",
              "      <td>Would \"clipper\" make a good cover for other e...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1653</td>\n",
              "      <td>What the clipper nay-sayers sound like to me....</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
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              "      <th>4</th>\n",
              "      <td>1654</td>\n",
              "      <td>alt.security.pgp\\nNntp-Posting-Host: bootes.c...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>2245</td>\n",
              "      <td>Re: Terraforming Venus: can it be done \"cheap...</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>2246</td>\n",
              "      <td>Re: *Doppelganger* \\nArticle-I.D.: mojo.1qkn6...</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>2247</td>\n",
              "      <td>Re: What if the USSR had reached the Moon fir...</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>2248</td>\n",
              "      <td>Re: DC-X Rollout Report\\nArticle-I.D.: topaz....</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>2249</td>\n",
              "      <td>Re: Vandalizing the sky.\\nOrganization: Locus...</td>\n",
              "      <td>14</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     index                                               Text  Label  \\\n",
              "0     1650   Privacy & Anonymity on the Internet FAQ \\nSup...     11   \n",
              "1     1651   The source of that announcement\\nOrganization...     11   \n",
              "2     1652   Would \"clipper\" make a good cover for other e...     11   \n",
              "3     1653   What the clipper nay-sayers sound like to me....     11   \n",
              "4     1654   alt.security.pgp\\nNntp-Posting-Host: bootes.c...     11   \n",
              "..     ...                                                ...    ...   \n",
              "595   2245   Re: Terraforming Venus: can it be done \"cheap...     14   \n",
              "596   2246   Re: *Doppelganger* \\nArticle-I.D.: mojo.1qkn6...     14   \n",
              "597   2247   Re: What if the USSR had reached the Moon fir...     14   \n",
              "598   2248   Re: DC-X Rollout Report\\nArticle-I.D.: topaz....     14   \n",
              "599   2249   Re: Vandalizing the sky.\\nOrganization: Locus...     14   \n",
              "\n",
              "    Class Name  \n",
              "0    sci.crypt  \n",
              "1    sci.crypt  \n",
              "2    sci.crypt  \n",
              "3    sci.crypt  \n",
              "4    sci.crypt  \n",
              "..         ...  \n",
              "595  sci.space  \n",
              "596  sci.space  \n",
              "597  sci.space  \n",
              "598  sci.space  \n",
              "599  sci.space  \n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Take a sample of each label category from df_train\n",
        "SAMPLE_SIZE = 150\n",
        "df_train = (df_train.groupby('Label', as_index = False)\n",
        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
        "                    .reset_index(drop=True))\n",
        "\n",
        "# Choose categories about science\n",
        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
        "\n",
        "# Reset the index\n",
        "df_train = df_train.reset_index()\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "UjTrEnmdIo5P"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Class Name\n",
              "sci.crypt          150\n",
              "sci.electronics    150\n",
              "sci.med            150\n",
              "sci.space          150\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train['Class Name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUgv8SOwXfAX"
      },
      "source": [
        "## Generate the embeddings\n",
        "\n",
        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API.\n",
        "\n",
        "The Gemini embedding model supports several task types, each tailored for a specific goal. Here’s a general overview of the available types and their applications:\n",
        "\n",
        "Task Type | Description\n",
        "---       | ---\n",
        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jkS_EWfAXcxc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 600/600 [06:11<00:00,  1.62it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "tqdm.pandas()\n",
        "\n",
        "def make_embed_text_fn(model):\n",
        "\n",
        "  def embed_fn(text: str) -> list[float]:\n",
        "    # Set the task_type to CLUSTERING.\n",
        "    result = client.models.embed_content(model=model,\n",
        "                                            contents=text,\n",
        "                                            config=types.EmbedContentConfig(\n",
        "                                                task_type=\"CLUSTERING\"))\n",
        "    return np.array(result.embeddings[0].values)\n",
        "\n",
        "  return embed_fn\n",
        "\n",
        "def create_embeddings(df):\n",
        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(MODEL_ID))\n",
        "  return df\n",
        "\n",
        "df_train = create_embeddings(df_train)\n",
        "df_train.drop('index', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNTjKcD_aluG"
      },
      "source": [
        "## Dimensionality reduction\n",
        "\n",
        "The dimension of the document embedding vector is 3072. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "BJDHDQmeZqy2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3072"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_train['Embeddings'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "S5-XU-twaoK6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(600, 3072)"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV-Y7iEtbAkm"
      },
      "source": [
        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "FhYKF-lObC04"
      },
      "outputs": [],
      "source": [
        "tsne = TSNE(random_state=0)\n",
        "tsne_results = tsne.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "31wdqnp_bH9B"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.326805</td>\n",
              "      <td>-15.291752</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.044193</td>\n",
              "      <td>-2.996266</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.786301</td>\n",
              "      <td>-6.652494</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.256178</td>\n",
              "      <td>-9.720546</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.185246</td>\n",
              "      <td>-8.945529</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>-25.796329</td>\n",
              "      <td>8.991899</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>-20.514021</td>\n",
              "      <td>0.129148</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>-22.480268</td>\n",
              "      <td>6.861408</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>-23.748753</td>\n",
              "      <td>14.050385</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>-22.166462</td>\n",
              "      <td>4.321325</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name\n",
              "0    39.326805 -15.291752  sci.crypt\n",
              "1    25.044193  -2.996266  sci.crypt\n",
              "2    30.786301  -6.652494  sci.crypt\n",
              "3    26.256178  -9.720546  sci.crypt\n",
              "4    41.185246  -8.945529  sci.crypt\n",
              "..         ...        ...        ...\n",
              "595 -25.796329   8.991899  sci.space\n",
              "596 -20.514021   0.129148  sci.space\n",
              "597 -22.480268   6.861408  sci.space\n",
              "598 -23.748753  14.050385  sci.space\n",
              "599 -22.166462   4.321325  sci.space\n",
              "\n",
              "[600 rows x 3 columns]"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "pTj8HfhpbJ9X"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'TSNE2')"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using t-SNE')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JQbX4pcMdBe"
      },
      "source": [
        "## Outlier detection\n",
        "\n",
        "To determine which points are anomalous, you will determine which points are inliers and outliers. Start by finding the centroid, or location that represents the center of the cluster, and use the distance to determine the points that are outliers.\n",
        "\n",
        "Start by getting the centroid of each category."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "nUIkLxtMK4qC"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sci.crypt</th>\n",
              "      <td>31.146711</td>\n",
              "      <td>-6.366441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.electronics</th>\n",
              "      <td>7.322525</td>\n",
              "      <td>19.477852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.med</th>\n",
              "      <td>-9.485087</td>\n",
              "      <td>-22.437294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.space</th>\n",
              "      <td>-23.256557</td>\n",
              "      <td>9.471162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     TSNE1      TSNE2\n",
              "Class Name                           \n",
              "sci.crypt        31.146711  -6.366441\n",
              "sci.electronics   7.322525  19.477852\n",
              "sci.med          -9.485087 -22.437294\n",
              "sci.space       -23.256557   9.471162"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_centroids(df_tsne):\n",
        "  # Get the centroid of each cluster\n",
        "  centroids = df_tsne.groupby('Class Name').mean()\n",
        "  return centroids\n",
        "\n",
        "centroids = get_centroids(df_tsne)\n",
        "centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "GJH4Oo6E-r_6"
      },
      "outputs": [],
      "source": [
        "def get_embedding_centroids(df):\n",
        "  emb_centroids = dict()\n",
        "  grouped = df.groupby('Class Name')\n",
        "  for c in grouped.groups:\n",
        "    sub_df = grouped.get_group(c)\n",
        "    # Get the centroid value of dimension 768\n",
        "    emb_centroids[c] = np.mean(sub_df['Embeddings'], axis=0)\n",
        "\n",
        "  return emb_centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "1tas9Yg4_iyq"
      },
      "outputs": [],
      "source": [
        "emb_c = get_embedding_centroids(df_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMvdYLjKl32a"
      },
      "source": [
        "Plot each centroid you have found against the rest of the points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpN02WY3Ogji"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the centroids against the cluster\n",
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using t-SNE with centroids')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onFfUf1XoEQW"
      },
      "source": [
        "Choose a radius. Anything beyond this bound from the centroid of that category is considered an outlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "87cDfNpvOu7f"
      },
      "outputs": [],
      "source": [
        "def calculate_euclidean_distance(p1, p2):\n",
        "  return np.sqrt(np.sum(np.square(p1 - p2)))\n",
        "\n",
        "def detect_outlier(df, emb_centroids, radius):\n",
        "  for idx, row in df.iterrows():\n",
        "    class_name = row['Class Name'] # Get class name of row\n",
        "    # Compare centroid distances\n",
        "    dist = calculate_euclidean_distance(row['Embeddings'],\n",
        "                                        emb_centroids[class_name])\n",
        "    df.at[idx, 'Outlier'] = dist > radius\n",
        "\n",
        "  return len(df[df['Outlier'] == True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "CsVsod5MKd3X"
      },
      "outputs": [],
      "source": [
        "range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()\n",
        "num_outliers = []\n",
        "for i in range_:\n",
        "  num_outliers.append(detect_outlier(df_train, emb_c, i))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "vReUSOjbNHQv"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot range_ and num_outliers\n",
        "fig = plt.figure(figsize = (14, 8))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plt.bar(list(map(str, range_)), num_outliers)\n",
        "plt.title(\"Number of outliers vs. distance of points from centroid\")\n",
        "plt.xlabel(\"Distance\")\n",
        "plt.ylabel(\"Number of outliers\")\n",
        "for i in range(len(range_)):\n",
        "  plt.text(i, num_outliers[i], num_outliers[i], ha = 'center')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNISxrzwGvBH"
      },
      "source": [
        "Depending on how sensitive you want your anomaly detector to be, you can choose which radius you would like to use. For now, 0.58 is used, but you can change this value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "PMNFFSDOTELn"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Label</th>\n",
              "      <th>Class Name</th>\n",
              "      <th>Embeddings</th>\n",
              "      <th>Outlier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Cryptography FAQ 03/10 - Basic Cryptology\\nOrg...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[-0.03167741, -0.00045672545, -0.01068269, -0....</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>[Rubick] Shortest Path Algorithm - Status?\\nO...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[-0.022985524, -0.024591176, -0.011402696, -0....</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Re: Trinomial-Based PRNG\\nOrganization: Schoo...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[-0.025036471, -0.005380305, -0.00593743, -0.0...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>Electric power line \"balls\"\\nArticle-I.D.: alm...</td>\n",
              "      <td>12</td>\n",
              "      <td>sci.electronics</td>\n",
              "      <td>[-0.014121288, 0.00074380956, 0.007506555, -0....</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Electrical wiring FAQ (was: A question about 1...</td>\n",
              "      <td>12</td>\n",
              "      <td>sci.electronics</td>\n",
              "      <td>[-0.0092245415, -0.022044295, 0.0032056107, -0...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Text  Label  \\\n",
              "90   Cryptography FAQ 03/10 - Basic Cryptology\\nOrg...     11   \n",
              "97    [Rubick] Shortest Path Algorithm - Status?\\nO...     11   \n",
              "147   Re: Trinomial-Based PRNG\\nOrganization: Schoo...     11   \n",
              "181  Electric power line \"balls\"\\nArticle-I.D.: alm...     12   \n",
              "237  Electrical wiring FAQ (was: A question about 1...     12   \n",
              "\n",
              "          Class Name                                         Embeddings  \\\n",
              "90         sci.crypt  [-0.03167741, -0.00045672545, -0.01068269, -0....   \n",
              "97         sci.crypt  [-0.022985524, -0.024591176, -0.011402696, -0....   \n",
              "147        sci.crypt  [-0.025036471, -0.005380305, -0.00593743, -0.0...   \n",
              "181  sci.electronics  [-0.014121288, 0.00074380956, 0.007506555, -0....   \n",
              "237  sci.electronics  [-0.0092245415, -0.022044295, 0.0032056107, -0...   \n",
              "\n",
              "    Outlier  \n",
              "90     True  \n",
              "97     True  \n",
              "147    True  \n",
              "181    True  \n",
              "237    True  "
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the points that are outliers\n",
        "RADIUS = 0.54\n",
        "detect_outlier(df_train, emb_c, RADIUS)\n",
        "df_outliers = df_train[df_train['Outlier'] == True]\n",
        "df_outliers.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "h_wbM5yYE4MS"
      },
      "outputs": [],
      "source": [
        "# Use the index to map the outlier points back to the projected TSNE points\n",
        "outliers_projected = df_tsne.loc[df_outliers['Outlier'].index]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCt4wfYdoTJz"
      },
      "source": [
        "Plot the outliers and denote them using a transparent red color."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "IrAKwBp0TaNu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'TSNE2')"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
        "# Draw a red circle around the outliers\n",
        "sns.scatterplot(data=outliers_projected, x='TSNE1', y='TSNE2', color='red', marker='o', alpha=0.5, s=90, label='Outliers')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news with outliers projected with t-SNE')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVm_A9HmGwEN"
      },
      "source": [
        "Use the index values of the datafames to print a few examples of what outliers can look like in each category. Here, the first data point from each category is printed out. Explore other points in each category to see data that are deemed as outliers, or anomalies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "lpZ-hcDvG13M"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Electric power line \"balls\"\n",
            "Article-I.D.: almaden.19930406.142616.248\n",
            "Lines: 4\n",
            "\n",
            "Power lines and airplanes don't mix. In areas where lines are strung very\n",
            "high, or where a lot of crop dusting takes place, or where there is danger\n",
            "of airplanes flying into the lines, they place these plastic balls on the\n",
            "lines so they are easier to spot.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_electronics_outliers = df_outliers[df_outliers['Class Name'] == 'sci.electronics']\n",
        "print(sci_electronics_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "APPg8TURJ9yt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LARSONIAN Astronomy and Physics\n",
            "Organization: University of Wisconsin Eau Claire\n",
            "Lines: 552\n",
            "\n",
            "\n",
            "\n",
            "                      LARSONIAN Astronomy and Physics\n",
            "\n",
            "               Orthodox physicists, astronomers, and astrophysicists \n",
            "          CLAIM to be looking for a \"Unified Field Theory\" in which all \n",
            "          of the forces of the universe can be explained with a single \n",
            "          set of laws or equations.  But they have been systematically \n",
            "          IGNORING or SUPPRESSING an excellent one for 30 years! \n",
            "\n",
            "               The late Physicist Dewey B. Larson's comprehensive \n",
            "          GENERAL UNIFIED Theory of the physical universe, which he \n",
            "          calls the \"Reciprocal System\", is built on two fundamental \n",
            "          postulates about the physical and mathematical natures of \n",
            "          space and time: \n",
            "    \n",
            "                \"The physical universe is composed ENTIRELY of ONE \n",
            "          component, MOTION, existing in THREE dimensions, in DISCRETE \n",
            "          UNITS, and in two RECIPROCAL forms, SPACE and TIME.\" \n",
            "    \n",
            "                \"The physical universe conforms to the relations of \n",
            "          ORDINARY COMMUTATIVE mathematics, its magnitudes are \n",
            "          ABSOLUTE, and its geometry is EUCLIDEAN.\" \n",
            "    \n",
            "               From these two postulates, Larson developed a COMPLETE \n",
            "          Theoretical Universe, using various combinations of \n",
            "          translational, vibrational, rotational, and vibrational-\n",
            "          rotational MOTIONS, the concepts of IN-ward and OUT-ward \n",
            "          SCALAR MOTIONS, and speeds in relation to the Speed of Light \n",
            "          . \n",
            "      \n",
            "               At each step in the development, Larson was able to \n",
            "          MATCH objects in his Theoretical Universe with objects in the \n",
            "          REAL physical universe, , even objects NOT YET \n",
            "          DISCOVERED THEN . \n",
            "          \n",
            "               And applying his Theory to his NEW model of the atom, \n",
            "          Larson was able to precisely and accurately CALCULATE inter-\n",
            "          atomic distances in crystals and molecules, compressibility \n",
            "          and thermal expansion of solids, and other properties of \n",
            "          matter. \n",
            "\n",
            "               All of this is described in good detail, with-OUT fancy \n",
            "          complex mathematics, in his books. \n",
            "    \n",
            "\n",
            "\n",
            "          BOOKS of Dewey B. Larson\n",
            "          \n",
            "               The following is a complete list of the late Physicist \n",
            "          Dewey B. Larson's books about his comprehensive GENERAL \n",
            "          UNIFIED Theory of the physical universe.  Some of the early \n",
            "          books are out of print now, but still available through \n",
            "          inter-library loan. \n",
            "    \n",
            "               \"The Structure of the Physical Universe\"  \n",
            "    \n",
            "               \"The Case AGAINST the Nuclear Atom\" \n",
            "    \n",
            "               \"Beyond Newton\"  \n",
            "    \n",
            "               \"New Light on Space and Time\"  \n",
            "    \n",
            "               \"Quasars and Pulsars\"  \n",
            "    \n",
            "               \"NOTHING BUT MOTION\"  \n",
            "                    [A $9.50 SUBSTITUTE for the $8.3 BILLION \"Super \n",
            "                                                            Collider\".] \n",
            "                    [The last four chapters EXPLAIN chemical bonding.]\n",
            "\n",
            "               \"The Neglected Facts of Science\"  \n",
            "     \n",
            "               \"THE UNIVERSE OF MOTION\" \n",
            "                    [FINAL SOLUTIONS to most ALL astrophysical\n",
            "                                                            mysteries.] \n",
            "      \n",
            "               \"BASIC PROPERTIES OF MATTER\" \n",
            "\n",
            "               All but the last of these books were published by North \n",
            "          Pacific Publishers, P.O. Box 13255, Portland, OR  97213, and \n",
            "          should be available via inter-library loan if your local \n",
            "          university or public library doesn't have each of them. \n",
            "\n",
            "               Several of them, INCLUDING the last one, are available \n",
            "          from: The International Society of Unified Science , \n",
            "          1680 E. Atkin Ave., Salt Lake City, Utah  84106.  This is the \n",
            "          organization that was started to promote Larson's Theory.  \n",
            "          They have other related publications, including the quarterly \n",
            "          journal \"RECIPROCITY\". \n",
            "\n",
            "          \n",
            "\n",
            "          Physicist Dewey B. Larson's Background\n",
            "    \n",
            "               Physicist Dewey B. Larson was a retired Engineer \n",
            "          .  He was about 91 years old when he \n",
            "          died in May 1989.  He had a Bachelor of Science Degree in \n",
            "          Engineering Science from Oregon State University.  He \n",
            "          developed his comprehensive GENERAL UNIFIED Theory of the \n",
            "          physical universe while trying to develop a way to COMPUTE \n",
            "          chemical properties based only on the elements used. \n",
            "    \n",
            "               Larson's lack of a fancy \"PH.D.\" degree might be one \n",
            "          reason that orthodox physicists are ignoring him, but it is \n",
            "          NOT A VALID REASON.  Sometimes it takes a relative outsider \n",
            "          to CLEARLY SEE THE FOREST THROUGH THE TREES.  At the same \n",
            "          time, it is clear from his books that he also knew ORTHODOX \n",
            "          physics and astronomy as well as ANY physicist or astronomer, \n",
            "   \n"
          ]
        }
      ],
      "source": [
        "sci_space_outliers = df_outliers[df_outliers['Class Name'] == 'sci.space']\n",
        "print(sci_space_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siaPlEJhh0pr"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that t-SNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. \n",
        "\n",
        "To learn how to use other services in the Gemini API, see the [Get started](../quickstarts/Get_started.ipynb) guide."
      ]
    }
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